Lamini is an enterprise LLM platform designed for software teams to develop and control their own LLMs. It offers built-in best practices for specializing LLMs on proprietary documents, improving performance, reducing hallucinations, offering citations, and ensuring safety. Lamini can be installed on-premise or on clouds securely and is the only platform for running LLMs on AMD GPUs and scaling to thousands with confidence.
Lamini
Enterprise LLM platform for developing and controlling custom LLMs with high accuracy.
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What is Lamini?
How to use
Use the Lamini library to train high-performing LLMs on large datasets. Install Lamini on-premise or on your cloud environment. Utilize built-in best practices for specializing LLMs on your proprietary data to improve performance and accuracy.
Core Features
- LLM fine-tuning
- Hallucination reduction
- Memory RAG
- Classifier Agent Toolkit
- Text-to-SQL agent building
- Function calling
- Secure deployment (on-premise, VPC, air-gapped)
Use Cases
- Building highly accurate text-to-SQL agents
- Automating manual classification tasks
- Connecting to external tools and APIs
- Factual reasoning chatbots
- Code assistants
- Customer service agents
FAQ
How does Lamini reduce hallucinations in LLMs?
Lamini uses built-in best practices for specializing LLMs on billions of proprietary documents to improve performance and reduce hallucinations by up to 95%.
Where can Lamini be deployed?
Lamini can be deployed in secure environments, including on-premise (even air-gapped) or VPC, ensuring your data remains private.
What kind of support does Lamini offer?
Lamini offers help and support through a dedicated form for reporting bugs, requesting features, or sharing feedback.
What models does Lamini support?
Lamini provides access to top open source models like Llama 3.1, Mistral v0.3, and Phi 3.
Pricing
On-demand
$0.50/1M tokens (inference), $0.50/step (tuning)
Pay as you go, new users get $300 free credit.
Reserved
Custom
Dedicated GPUs from Lamini's cluster, unlimited tuning and inference.
Self-managed
Custom
Run Lamini in your own secure environment, pay per software license.
Pros & Cons
Pros
- Reduces LLM hallucinations significantly (up to 95%)
- Enables building smaller, faster LLMs and agents
- Supports secure deployment in various environments
- Offers high accuracy for content classification and text-to-SQL
- Reduces engineering time for fine-tuning models
- Provides tools for evaluating LLM performance
Cons
- Pricing is not transparent and requires contacting for details for some plans
- May require some machine learning expertise to fully leverage the platform
- Reliance on AMD GPUs for optimal performance